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on005429
Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)

EEG Zarr

Raw EEG recordings from 15 participants who each completed three auditory oddball sequences: the Optimum-1 paradigm for mismatch negativity (MMN), a learning-oddball paradigm for P3b, and the local–global paradigm for local and global effects. The dataset supports comparison of within-individual sensitivity of MMN and P3b effects across specialized oddball sequences and the local–global paradigm. Corrected event files, stimulation sequence definitions and stimulus presentation code are included.

Participants

15

Channels

64 (10-10)

Size

16.5 GB

Version

v1.0.0

Updated

Jun 27, 2026

by Renate Rutiku et al. OpenNeuro ds005429
nm000346
Castillos et al. 2023, m-sequence c-VEP, 100% amplitude depth: Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience

EEG Zarr

EEG dataset of a 4-class code-modulated visual evoked potential (c-VEP) reactive brain-computer interface study with 12 healthy participants. This subset contains the m-sequence c-VEP condition at 100% amplitude depth, recorded with a 32-channel BrainProducts LiveAmp system at 500 Hz. The study compared burst and m-sequence codes at two amplitude depths, assessing classification accuracy with minimal calibration data and user experience.

Participants

12

Channels

32 (10-10)

Citations

18

HED

v8.4.0

Size

352 MB

Version

v1.0.3

Updated

Oct 8, 2026

by Kalou Cabrera Castillos et al.
nm000339
Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population

EEG Zarr

A large, longitudinal EEG dataset collected while 62 healthy adults learned to control a sensorimotor rhythm-based brain-computer interface. Participants completed 7-11 online training sessions of continuous cursor control using motor imagery of the left hand, right hand, both hands, and rest. In total the dataset comprises 598 sessions and over 250,000 trials, offering a resource for designing and evaluating BCI algorithms and for studying BCI learning.

Participants

62

Channels

60 (10-10)

Citations

63

HED

v8.4.0

Size

722 GB

Version

v1.0.4

Updated

Aug 18, 2026

by James R. Stieger et al.
on007949
Auditory Streaming and Interoception

EEG Zarr

EEG dataset on auditory streaming and interoception, collected at the University of Chicago by Veillette, Joshi, Li, and Nusbaum. Data are organized in BIDS format (BrainVision files) and released under a CC0 license.

Participants

22

Channels

1 (other)

Size

7.25 GB

Version

v1.0.0

Updated

Jun 19, 2026

by John Veillette et al. OpenNeuro ds007949
nm000193
Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams

EEG Zarr

EEG dataset from 11 healthy participants performing a 3-class auditory brain-computer interface task based on auditory stream segregation. Musical tones forming three tone streams were presented to the right ear, and participants attended to one stream while counting target stimuli, which elicited P300 activity. Recordings comprise 64-channel EEG and 2-channel EOG at 1000 Hz. Classification based on Riemannian geometry achieved over 80% accuracy for five subjects and over 75% for nine subjects in 10-fold cross-validation.

Participants

11

Channels

64 (10-10)

Citations

10

HED

v8.4.0

Size

6.31 GB

Version

v1.0.5

Updated

Oct 8, 2026

by Simon Kojima et al.
on007895
Auditory Streaming and Interoception

EEG Zarr

EEG dataset on auditory streaming and interoception, collected at the University of Chicago and organized in BIDS format. The README gives few details of the experimental design, so the description rests mainly on the dataset name and metadata.

Participants

22

Channels

1 (other)

Size

7.25 GB

Version

v1.0.0

Updated

Jun 19, 2026

by John Veillette et al. OpenNeuro ds007895
on003645
Face processing MEEG dataset with HED annotation

ANAT BEH EEG MEG Zarr

A multi-modal face processing dataset combining simultaneously recorded MEG and EEG with structural MRI from 18 participants. Participants viewed famous, unfamiliar, and scrambled faces and made a symmetry judgment by button press. The data are a curated repackaging of the MEEG portion of ds000117, with reorganized event logs, Hierarchical Event Descriptor (HED) annotations, and EEGLAB-format EEG.

Participants

19

Citations

80

HED

v8.1.0

Size

106 GB

Version

v1.0.0

Updated

Mar 9, 2026

by Daniel G. Wakeman et al. OpenNeuro ds003645
nm000344
Castillos et al. 2023, Burst c-VEP, 100% amplitude depth: Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience

EEG Zarr

EEG recordings from 12 healthy participants performing an offline 4-class code-modulated visual evoked potential (c-VEP) task with burst stimulation sequences at 100% amplitude depth. The dataset accompanies a study comparing burst c-VEP codes with m-sequences to improve classification accuracy, reduce calibration data and improve user experience in reactive brain-computer interfaces. Data were acquired with a 32-channel BrainProducts LiveAmp system at 500 Hz.

Participants

12

Channels

32 (10-10)

Citations

18

HED

v8.4.0

Size

352 MB

Version

v1.0.3

Updated

Oct 8, 2026

by Kalou Cabrera Castillos et al.
nm000206
Neuroergonomic 2021 dataset

EEG Zarr

This dataset contains 62-channel EEG recorded at 500 Hz from 15 healthy participants across two sessions. It combines resting-state recordings with three difficulty levels (easy, medium, difficult) of the MATB-II multitasking paradigm, and was designed for passive brain-computer interface research on cross-session mental workload estimation. The BIDS version is a MOABB-derived conversion of the COG-BCI database, with HED event annotations.

Participants

15

Channels

61 (10-10)

Citations

70

HED

v8.4.0

Size

1.22 GB

Version

v1.0.0

Updated

Jun 2, 2026

by Marcel F. Hinss et al.
nm000340
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study J)

EEG Zarr

Study J of the bigP3BCI collection, which provides EEG recordings from 20 healthy participants using a P300-based speller. The task used a 9x8 character grid with a performance-based row-column paradigm, and 16 EEG channels were recorded at 256 Hz. Target and non-target flash events are annotated with HED tags, and the dataset supports machine-learning benchmarking of P300 brain-computer interfaces.

Participants

20

Channels

16 (10-10)

Citations

2

HED

v8.4.0

Size

1.49 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.